Instructions to use ninja/frandy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ninja/frandy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ninja/frandy")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("ninja/frandy") model = AutoModelForCTC.from_pretrained("ninja/frandy") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f10520b578da2f0beb7eff56eef77e2e1c10e7338b2b7f7b4713f8bcffb1cb40
- Size of remote file:
- 378 MB
- SHA256:
- a5689b784539de4db74f59581a6d5a958fd3676e9a43a695cb9c0083e15454fb
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